As background for my invention it is noted that several methods have been implemented for the purpose of interpreting an image on a medium for its correspondence with the characters of an alphabet (1-7). These generally come under the category of character recognition or optical character recognition (OCR). Some of these methods do the recognition regardless of how the characters were generated on the medium (6-7). Another subset of these methods are general classification schemes with respect to a general set of features (5). Another subset of these methods further distinguish characteristics an image of text (1-4). Two of the referenced methods have the sole objective (2) or partial objective (1) to discriminate whether the image has been either machine printed or handprinted. The advantage of making this discrimination is that one can use different OCR methods for two different types of text, with a corresponding increase in accuracy and/or speed of recognition.
This prior art uses methods and implementation that are different than we espouse, and therefore, does not have the performance of discrimination. My method uses a spectral analysis of features of the image and uses a feedforward neural network to discriminate between the spectrum of the two types of printing.